Coronavirus US - USA Facts
davegoblue
2022-09-13
Background
This module builds on code contained in Coronavirus_Statistics_USAF_v006.Rmd. This file includes the latest code for analyzing data from USA Facts. USA Facts maintains data on cases and deaths by county for coronavirus in the US. Downloaded data are unique by county with date as a column and a separate file for each of cases, deaths, and population.
The intent of this code is to source updated functions that allow for readRunUSAFacts() to be run to obtain, read, process, and analyze data from USA Facts.
Sourcing Functions
The tidyverse library is loaded, and the functions used for CDC daily processing are sourced. Additionally, specific functions for USA Facts are also sourced:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")
source("./Coronavirus_USAF_Functions_v001.R")Further, the mapping file specific to USA Facts is sourced:
source("./Coronavirus_USAF_Default_Mappings_v002.R")Updated functions for diagnoseClusters(), createDetailedSummaries(), createSummary(), and helperSummaryMap() are included in Coronavirus_USAF_Functions_v001.R. These functions should be checked for consistency with state-level data with just a single copy kept later.
Example Process
The latest county-level burden data are downloaded:
readList <- list("usafCase"="./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20220913.csv",
"usafDeath"="./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20220913.csv"
)
compareList <- list("usafCase"=readFromRDS("cty_newdata_20220807")$dfRaw$usafCase,
"usafDeath"=readFromRDS("cty_newdata_20220807")$dfRaw$usafDeath
)
# Use existing clusters
cty_newdata_20220913 <- readRunUSAFacts(maxDate="2022-09-11",
downloadTo=lapply(readList,
FUN=function(x) if(file.exists(x)) NA else x
),
readFrom=readList,
compareFile=compareList,
writeLog="./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log",
ovrwriteLog=TRUE,
useClusters=readFromRDS("cty_newdata_20210813")$useClusters,
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20220913.csv## Rows: 3193 Columns: 962
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (959): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 2 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log##
##
## ***Differences of at least 0 and at least 0.1%
##
## 128 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log## Rows: 3193 Columns: 962
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (959): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 3 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log
##
##
## ***Differences of at least 0 and at least 0.1%
##
## 108 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20220913_chk_v005.log
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType cases new_cases n
## <chr> <dbl> <dbl> <dbl>
## 1 before 3.45e+10 93305986 3058894
## 2 after 3.43e+10 91158859 3010036
## 3 pctchg 7.16e- 3 0.0230 0.0160
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType deaths new_deaths n
## <chr> <dbl> <dbl> <dbl>
## 1 before 5.12e+8 1034939 3058894
## 2 after 4.95e+8 964043 3010036
## 3 pctchg 3.23e-2 0.0685 0.0160
## NULL
# Plot all counties based on closest cluster
sparseCountyClusterMap(cty_newdata_20220913$useClusters,
caption="Includes only counties with 25k+ population",
brewPalette="viridis"
)
# Save the refreshed file
saveToRDS(cty_newdata_20220913, ovrWriteError=FALSE)Vaccines data are also updated, though the process needs to integrate previous data:
cty_vaxdata_20220914 <- processCountyVaccines(loc="./RInputFiles/Coronavirus/county_vaccine_20220914.csv",
ctyList=cty_newdata_20220913,
minDateCD=c("2022-06-09", "2022-06-09"),
maxDateCD="2022-09-01"
)## Rows: 78961 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Date, FIPS, Recip_County, Recip_State, SVI_CTGY, Metro_status
## dbl (66): MMWR_week, Completeness_pct, Administered_Dose1_Recip, Administere...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## Records from other than 50 states and DC:
## # A tibble: 9 × 2
## state n
## <chr> <int>
## 1 AS 24
## 2 FM 25
## 3 GU 48
## 4 MH 24
## 5 MP 24
## 6 PR 1901
## 7 PW 24
## 8 VI 96
## 9 <NA> 17
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
##
## Count of NA records by column
## state FIPS popgte65_minpop popgte65_maxpop popgte65_nnA
## 0 0 0 0 0
## n
## 0
##
## Records where minimum and maximum population differ# A tibble: 0 × 5
## # … with 5 variables: state <chr>, FIPS <chr>, age <chr>, minpop <dbl>,
## # maxpop <dbl>
## # ℹ Use `colnames()` to see all variable names
##
##
##
## Will run with parameters:
## burdenVar: cpm dpm
## vaxVar: vxcpoppct vxcpoppct
## minDateCD: 2022-06-09 2022-06-09
## maxDateCD: 2022-09-01 2022-09-01## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -289154037 -1505413 -90865 1548407 62031339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20807.226 2187.230 9.513 <2e-16 ***
## vaxMetric 8.061 33.809 0.238 0.812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7367000 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 1.82e-05, Adjusted R-squared: -0.0003019
## F-statistic: 0.05684 on 1 and 3124 DF, p-value: 0.8116
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -288389946 -1463009 -64586 1597997 65055859
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 12176.47 7942.37 1.533 0.125352
## type>500k 29162.62 4682.01 6.229 5.34e-10 ***
## type100k-500k 17769.84 4666.92 3.808 0.000143 ***
## type25k-100k 17521.42 5263.39 3.329 0.000882 ***
## vaxMetric:type<25k 175.75 159.92 1.099 0.271861
## vaxMetric:type>500k -110.85 66.36 -1.670 0.094933 .
## vaxMetric:type100k-500k 60.38 74.96 0.805 0.420630
## vaxMetric:type25k-100k 66.14 99.33 0.666 0.505574
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7368000 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.4677, Adjusted R-squared: 0.4663
## F-statistic: 342.4 on 8 and 3118 DF, p-value: < 2.2e-16## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3529509 -12224 -2495 14425 697276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 298.9805 29.5946 10.103 <2e-16 ***
## vaxMetric -4.0106 0.4575 -8.767 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 99680 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.02401, Adjusted R-squared: 0.0237
## F-statistic: 76.86 on 1 and 3124 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3480323 -13174 -5106 10332 707402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 130.2314 107.0422 1.217 0.22384
## type>500k 383.7067 63.1011 6.081 1.34e-09 ***
## type100k-500k 99.6779 62.8978 1.585 0.11312
## type25k-100k 207.0272 70.9366 2.918 0.00354 **
## vaxMetric:type<25k -0.3702 2.1553 -0.172 0.86365
## vaxMetric:type>500k -5.4661 0.8943 -6.112 1.11e-09 ***
## vaxMetric:type100k-500k -0.5040 1.0103 -0.499 0.61791
## vaxMetric:type25k-100k -1.8241 1.3388 -1.362 0.17314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 99300 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.05224, Adjusted R-squared: 0.04981
## F-statistic: 21.48 on 8 and 3118 DF, p-value: < 2.2e-16# Save the refreshed file
saveToRDS(cty_vaxdata_20220914, ovrWriteError=FALSE)County-level data are post-processed:
cty_postdata_20220913 <- postProcessCountyData(lstCtyBurden=cty_newdata_20220913$dfPerCapita,
lstCtyVax=cty_vaxdata_20220914$vaxFix,
lstState=readFromRDS("cdc_daily_220902")$dfPerCapita
)##
## Parameter maxDate is: 2022-09-01
# Save the refreshed file
saveToRDS(cty_postdata_20220913, ovrWriteError=FALSE)Additional post-processing steps are run:
# Step 1a: Burden comparisons for aggregated states
additionalCountyPostProcess(cty_postdata_20220913, p1CompareStates=c(state.abb, "DC"), p1AggData=TRUE)## Warning: Removed 6 row(s) containing missing values (geom_path).
# Step 1: Burden aggregation for key states
# Step 2: vaccine comparisons
# Step 3: Scoring updates (and errors)
# Step 4: New rolling data (28-day default with ceilings 50000 CPM, 500 DPM)
additionalCountyPostProcess(cty_postdata_20220913,
p1CompareStates=c("GA", "FL", "NE"),
p2VaxStates=c("MA", "HI", "TX", "VA", "VT", "GA", "CO", "SD"),
p3VaxTimes=sort(c("2022-01-01", "2022-08-31")),
p4DF=cty_newdata_20220913$dfPerCapita
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 1 row(s) containing missing values (geom_path).
Additional plots are also updated:
# Creating the vaccine and burden data
tmpVaxBurden <- createVaxBurdenData(lstVax=cty_vaxdata_20220914, lstBurden=cty_newdata_20220913)
# Nationwide aggregation, excluding problem states
problemStates <- c("VA", "TX", "SD", "HI", "GA", "CO", "GA", "FL", "NE", "VA")
useStates <- state.abb
tmpCountyList <- tmpVaxBurden$ctyPop %>%
filter(state %in% useStates, !(state %in% problemStates)) %>%
left_join(filter(tmpVaxBurden$dfVaxBurden, name=="vxcpoppct", date==max(date)), by="countyFIPS") %>%
mutate(vaxPct=percent_rank(value),
vaxBucket=case_when(vaxPct <= .25 ~ "3. Low", vaxPct >= .75 ~ "1. High", TRUE ~ "2. Medium")
) %>%
split(f=.$vaxBucket)
# Plot of absolute burden
plotVaxBurdenData(tmpVaxBurden,
ctyPlot=lapply(tmpCountyList, FUN=function(x) x %>% rename(bucket=vaxBucket)),
plotTitle="Counties in states with continuous county data"
)
# Plots of burden relative to May 2021
plotVaxBurdenData(tmpVaxBurden,
ctyPlot=lapply(tmpCountyList, FUN=function(x) x %>% rename(bucket=vaxBucket)),
plotTitle="Counties in states with continuous county data",
scaleToDate="2021-05-01"
)
Multiple data anomalies need to be addressed - vaccines data covering only a short time period, counties significantly revising burden downwards, etc.
Counties with significant negative burden are investigated:
keyRatio <- cty_newdata_20220913$dfPerCapita %>%
select(countyFIPS, state, date, cases, deaths) %>%
group_by(countyFIPS, state) %>%
summarize(keyRatDeath=sum(ifelse(date==max(date), deaths, 0)/max(deaths)),
keyRatCases=sum(ifelse(date==max(date), cases, 0)/max(cases)),
.groups="drop"
)
keyRatio %>%
mutate(across(where(is.numeric), .fns=function(x) round(x, 3))) %>%
count(keyRatCases, keyRatDeath) %>%
ggplot(aes(x=keyRatCases, y=keyRatDeath)) +
geom_point(aes(size=n)) +
lims(x=c(0, 1), y=c(0, 1)) +
labs(x="Latest cases vs. maximum cases",
y="Latest deaths vs. maximum deaths",
title="County-level restatement"
)## Warning: Removed 6 rows containing missing values (geom_point).
keyRatio %>%
mutate(across(where(is.numeric), .fns=function(x) round(x, 3))) %>%
count(keyRatCases, keyRatDeath) %>%
filter(keyRatCases < 1 | keyRatDeath < 1) %>%
ggplot(aes(x=keyRatCases, y=keyRatDeath)) +
geom_point(aes(size=n)) +
lims(x=c(0, 1), y=c(0, 1)) +
labs(x="Latest cases vs. maximum cases",
y="Latest deaths vs. maximum deaths",
title="County-level restatement",
subtitle="Only counties with at least one ratio .999 or lower included"
)## Warning: Removed 4 rows containing missing values (geom_point).
Restatement of deaths is much more common than restatement of cases. Counties with declines are further explored:
decStatus <- keyRatio %>%
mutate(rd=case_when(is.na(keyRatDeath) ~ -1,
keyRatDeath==1 ~ 1,
keyRatDeath>=.95 ~ .95,
keyRatDeath>=.9 ~ .9,
keyRatDeath>=.75 ~ .75,
keyRatDeath>=.5 ~ .5,
TRUE ~ 0
)
)
decStatus %>%
count(rd)## # A tibble: 7 × 2
## rd n
## <dbl> <int>
## 1 -1 20
## 2 0 17
## 3 0.5 23
## 4 0.75 61
## 5 0.9 34
## 6 0.95 118
## 7 1 2869decStatus %>%
count(rd, state) %>%
filter(rd<.95, rd>=0, n>1) %>%
arrange(rd, -n)## # A tibble: 18 × 3
## rd state n
## <dbl> <chr> <int>
## 1 0 NE 4
## 2 0 AK 3
## 3 0 TX 3
## 4 0 MA 2
## 5 0.5 IL 10
## 6 0.5 NE 9
## 7 0.5 AK 2
## 8 0.75 IL 37
## 9 0.75 MA 11
## 10 0.75 NE 4
## 11 0.75 KS 3
## 12 0.75 VA 2
## 13 0.9 IL 17
## 14 0.9 NE 4
## 15 0.9 CA 2
## 16 0.9 CO 2
## 17 0.9 FL 2
## 18 0.9 VA 2cty_newdata_20220913$dfPerCapita %>%
left_join(select(decStatus, countyFIPS, state, rd), by=c("countyFIPS", "state")) %>%
group_by(rd, date) %>%
summarize(cases=sum(cases), deaths=sum(deaths), n=n(), .groups="drop") %>%
mutate(labFacet=paste0(rd, " (n=", n, ")")) %>%
ggplot(aes(x=date, y=deaths)) +
geom_line() +
facet_wrap(~labFacet, scales="free_y") +
labs(x=NULL,
y="Reported deaths",
title="Reported deaths, facetted by change from maximum"
)
Illinois and Nebraska appear to be main drivers of reported declines (discontinuities) in deaths. Reporting is potentially lagged, as much of the issue appears to be recent.
Restatement of cases is also explored:
decStatus <- keyRatio %>%
mutate(rd=case_when(is.na(keyRatCases) ~ -1,
keyRatCases==1 ~ 1,
keyRatCases>=.99 ~ .99,
keyRatCases>=.95 ~ .95,
keyRatCases>=.5 ~ .5,
TRUE ~ 0
)
)
decStatus %>%
count(rd)## # A tibble: 6 × 2
## rd n
## <dbl> <int>
## 1 -1 2
## 2 0 4
## 3 0.5 10
## 4 0.95 16
## 5 0.99 69
## 6 1 3041decStatus %>%
count(rd, state) %>%
filter(rd<.99, rd>=0, n>1) %>%
arrange(rd, -n)## # A tibble: 5 × 3
## rd state n
## <dbl> <chr> <int>
## 1 0 TX 3
## 2 0.5 NV 4
## 3 0.5 UT 2
## 4 0.95 NE 6
## 5 0.95 VA 6cty_newdata_20220913$dfPerCapita %>%
left_join(select(decStatus, countyFIPS, state, rd), by=c("countyFIPS", "state")) %>%
group_by(rd, date) %>%
summarize(cases=sum(cases), deaths=sum(deaths), n=n(), .groups="drop") %>%
mutate(labFacet=paste0(rd, " (n=", n, ")")) %>%
ggplot(aes(x=date, y=cases)) +
geom_line() +
facet_wrap(~labFacet, scales="free_y") +
labs(x=NULL,
y="Reported cases",
title="Reported cases, facetted by change from maximum"
)
In aggregate, the cases look OK, with the exception of a few counties that may have incomplete reporting in the most recent time period. Specific county declines are further explored:
decStatus <- keyRatio %>%
mutate(rd=case_when(is.na(keyRatDeath) ~ -1,
keyRatDeath==1 ~ 1,
keyRatDeath>=.95 ~ .95,
keyRatDeath>=.9 ~ .9,
keyRatDeath>=.75 ~ .75,
keyRatDeath>=.5 ~ .5,
TRUE ~ 0
)
)
decStatus %>%
count(rd)## # A tibble: 7 × 2
## rd n
## <dbl> <int>
## 1 -1 20
## 2 0 17
## 3 0.5 23
## 4 0.75 61
## 5 0.9 34
## 6 0.95 118
## 7 1 2869decStatus %>%
count(rd, state) %>%
filter(rd<.95, rd>=0, n>1) %>%
arrange(rd, -n)## # A tibble: 18 × 3
## rd state n
## <dbl> <chr> <int>
## 1 0 NE 4
## 2 0 AK 3
## 3 0 TX 3
## 4 0 MA 2
## 5 0.5 IL 10
## 6 0.5 NE 9
## 7 0.5 AK 2
## 8 0.75 IL 37
## 9 0.75 MA 11
## 10 0.75 NE 4
## 11 0.75 KS 3
## 12 0.75 VA 2
## 13 0.9 IL 17
## 14 0.9 NE 4
## 15 0.9 CA 2
## 16 0.9 CO 2
## 17 0.9 FL 2
## 18 0.9 VA 2cty_newdata_20220913$dfPerCapita %>%
left_join(select(decStatus, countyFIPS, state, rd), by=c("countyFIPS", "state")) %>%
filter(rd > 0, rd < 0.9) %>%
ggplot(aes(x=date, y=deaths)) +
geom_line(aes(group=countyFIPS, color=state)) +
facet_wrap(~rd, scales="free_y") +
labs(x=NULL,
y="Reported deaths by county",
title="Reported deaths, facetted by change from maximum"
)
There are two separate issues - some counties appear to have incomplete data in the latest time period, while other counties appear to have significant negative restatement of data earlier in 2022
The ratio process is updated:
keyRatioDate <- cty_newdata_20220913$dfPerCapita %>%
select(countyFIPS, state, date, cases, deaths) %>%
pivot_longer(-c(countyFIPS, state, date)) %>%
arrange(countyFIPS, state, name, date) %>%
group_by(countyFIPS, state, name) %>%
mutate(ratMax=value/max(value, na.rm=TRUE), cumMax=value/cummax(value)) %>%
ungroup()
keyRatioDate## # A tibble: 6,020,072 × 7
## countyFIPS state date name value ratMax cumMax
## <chr> <chr> <date> <chr> <dbl> <dbl> <dbl>
## 1 01001 AL 2020-01-22 cases 0 0 NaN
## 2 01001 AL 2020-01-23 cases 0 0 NaN
## 3 01001 AL 2020-01-24 cases 0 0 NaN
## 4 01001 AL 2020-01-25 cases 0 0 NaN
## 5 01001 AL 2020-01-26 cases 0 0 NaN
## 6 01001 AL 2020-01-27 cases 0 0 NaN
## 7 01001 AL 2020-01-28 cases 0 0 NaN
## 8 01001 AL 2020-01-29 cases 0 0 NaN
## 9 01001 AL 2020-01-30 cases 0 0 NaN
## 10 01001 AL 2020-01-31 cases 0 0 NaN
## # … with 6,020,062 more rows
## # ℹ Use `print(n = ...)` to see more rowsdfMinMax <- keyRatioDate %>%
filter(!is.na(cumMax)) %>%
group_by(countyFIPS, name) %>%
summarize(minMax=min(cumMax), .groups="drop")
dfMinMax %>%
ggplot(aes(x=minMax)) +
geom_density(aes(group=name, color=name)) +
labs(title="Ratio of burden vs. cumulative maximum of burden (expected to be 1 for ascending sequence)",
subtitle="Lowest value per county and metric plotted",
x="Lowest value of daily burden vs. cumulative maximum of burden",
y="Density"
) +
scale_color_discrete("Metric")
dfMinMax %>%
filter(minMax == 0, name=="deaths") %>%
select(countyFIPS) %>%
left_join(cty_newdata_20220913$dfPerCapita, by="countyFIPS") %>%
ggplot(aes(x=date, y=deaths)) +
geom_line(aes(group=countyFIPS)) +
facet_wrap(~countyFIPS, scales="free_y") +
labs(title="Reported deaths by counties with zero following non-zero", x=NULL, y="Reported deaths")
While some of the declines are anomalous, others appear to be curves that are either very low volume or smooth on a rolling-7 basis
The process is repeated to examine issues by state:
keyRatioDateState <- cty_newdata_20220913$dfPerCapita %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
pivot_longer(-c(state, date)) %>%
arrange(state, name, date) %>%
group_by(state, name) %>%
mutate(ratMax=value/max(value, na.rm=TRUE), cumMax=ifelse(cummax(value)==0, 1, value/cummax(value))) %>%
ungroup()
keyRatioDateState## # A tibble: 97,716 × 6
## state date name value ratMax cumMax
## <chr> <date> <chr> <dbl> <dbl> <dbl>
## 1 AK 2020-01-22 cases 0 0 1
## 2 AK 2020-01-23 cases 0 0 1
## 3 AK 2020-01-24 cases 0 0 1
## 4 AK 2020-01-25 cases 0 0 1
## 5 AK 2020-01-26 cases 0 0 1
## 6 AK 2020-01-27 cases 0 0 1
## 7 AK 2020-01-28 cases 0 0 1
## 8 AK 2020-01-29 cases 0 0 1
## 9 AK 2020-01-30 cases 0 0 1
## 10 AK 2020-01-31 cases 0 0 1
## # … with 97,706 more rows
## # ℹ Use `print(n = ...)` to see more rowsdfMinMaxState <- keyRatioDateState %>%
filter(!is.na(cumMax)) %>%
group_by(state, name) %>%
summarize(minMax=min(cumMax), .groups="drop")
dfMinMaxState %>%
ggplot(aes(x=minMax)) +
geom_density(aes(group=name, color=name)) +
labs(title="Ratio of burden vs. cumulative maximum of burden (expected to be 1 for ascending sequence)",
subtitle="Lowest value per state and metric plotted",
x="Lowest value of daily burden vs. cumulative maximum of burden",
y="Density"
) +
scale_color_discrete("Metric")
dfMinMaxState %>%
filter(minMax < 0.95, name=="deaths") %>%
select(state) %>%
left_join(cty_newdata_20220913$dfPerCapita, by="state") %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
ggplot(aes(x=date, y=deaths)) +
geom_line(aes(group=state)) +
facet_wrap(~state, scales="free_y") +
labs(title="Reported deaths by states with meaningfully non-ascending trend", x=NULL, y="Reported deaths")
Significant restatements appear to be in MA, while missing recent data appears to be in IL. It is unclear if MO and NE are still reporting county-level deaths. The data is potentially less complete and accurate than in previous iterations
The definition of a decline is modified to be min(cur-cummax)/max, so that declines of 1 or 2 early in the data are not flagged as major percentage changes:
keyRatioDateState_v2 <- cty_newdata_20220913$dfPerCapita %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
pivot_longer(-c(state, date)) %>%
arrange(state, name, date) %>%
group_by(state, name) %>%
mutate(ratMax=value/max(value, na.rm=TRUE),
cumMax=ifelse(cummax(value)==0, 1, (value-cummax(value))/max(value, na.rm=TRUE))
) %>%
ungroup()
keyRatioDateState_v2## # A tibble: 97,716 × 6
## state date name value ratMax cumMax
## <chr> <date> <chr> <dbl> <dbl> <dbl>
## 1 AK 2020-01-22 cases 0 0 1
## 2 AK 2020-01-23 cases 0 0 1
## 3 AK 2020-01-24 cases 0 0 1
## 4 AK 2020-01-25 cases 0 0 1
## 5 AK 2020-01-26 cases 0 0 1
## 6 AK 2020-01-27 cases 0 0 1
## 7 AK 2020-01-28 cases 0 0 1
## 8 AK 2020-01-29 cases 0 0 1
## 9 AK 2020-01-30 cases 0 0 1
## 10 AK 2020-01-31 cases 0 0 1
## # … with 97,706 more rows
## # ℹ Use `print(n = ...)` to see more rowsdfMinMaxState_v2 <- keyRatioDateState_v2 %>%
filter(!is.na(cumMax)) %>%
group_by(state, name) %>%
summarize(minMax=min(cumMax), .groups="drop")
dfMinMaxState_v2 %>%
ggplot(aes(x=fct_reorder(state, -minMax, min), y=1+minMax)) +
geom_col(fill="lightblue") +
geom_text(aes(label=round(1+minMax, 2)), hjust=0) +
labs(title="Ratio of burden vs. cumulative maximum of burden (expected to be 1 for ascending sequence)",
subtitle="Lowest value of 1 + (value - cummax(value)) / max(value)",
y="Lowest value",
x=NULL
) +
coord_flip() +
facet_wrap(~name)
dfMinMaxState_v2 %>%
filter(state %in% c("IL", "TX", "MA", "NE")) %>%
select(state) %>%
left_join(cty_newdata_20220913$dfPerCapita, by="state") %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
ggplot(aes(x=date, y=deaths)) +
geom_line(aes(group=state)) +
facet_wrap(~state, scales="free_y") +
labs(title="Reported deaths by states with meaningfully non-ascending trend", x=NULL, y="Reported deaths")
dfMinMaxState_v2 %>%
filter(state %in% c("IL", "WY")) %>%
select(state) %>%
left_join(cty_newdata_20220913$dfPerCapita, by="state") %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
ggplot(aes(x=date, y=cases)) +
geom_line(aes(group=state)) +
facet_wrap(~state, scales="free_y") +
labs(title="Reported cases by states with meaningfully non-ascending trend", x=NULL, y="Reported cases")
The updated methodology better flags states with meaningful restatement problems
The process is converted to functional form:
# Function to calculate distance from global maxima and previous maxima (including self)
findDeltaFromMax <- function(df, groupBy=c(), timeVar="date", numVars=NULL) {
# FUNCTION ARGUMENTS:
# df: a data frame
# groupBy: levels to which the final data should be aggregated
# timeVar: time variable to which data should be aggregated
# numVars: numeric variables to be summarized (NULL means all numeric variables)
# Find numVars if not provided
if(is.null(numVars)) numVars <- df %>% select(where(is.numeric)) %>% names %>% setdiff(groupBy)
df %>%
group_by_at(all_of(c(groupBy, timeVar))) %>%
summarize(across(all_of(numVars), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop") %>%
pivot_longer(all_of(numVars)) %>%
arrange(across(all_of(c(groupBy, "name", timeVar)))) %>%
group_by_at(all_of(c(groupBy, "name"))) %>%
mutate(ratMax=value/max(value, na.rm=TRUE),
cumMax=ifelse(cummax(value)==0, 1, value/cummax(value)),
delMax=ifelse(cummax(value)==0, 1, (value-cummax(value))/max(value, na.rm=TRUE))
) %>%
ungroup()
}
# Check for states
dfTest <- findDeltaFromMax(cty_newdata_20220913$dfPerCapita, groupBy="state", numVar=c("cases", "deaths"))
identical(dfTest %>% select(-delMax), keyRatioDateState)## [1] TRUEidentical(dfTest %>% select(-cumMax) %>% rename(cumMax=delMax), keyRatioDateState_v2)## [1] TRUE# Check for counties - old approach output NaN rather than 1 when cummax(value)=0
dfTest_v2 <- findDeltaFromMax(cty_newdata_20220913$dfPerCapita,
groupBy=c("countyFIPS", "state"),
numVar=c("cases", "deaths")
)
identical(dfTest_v2 %>% select(-delMax, -cumMax), keyRatioDate %>% select(-cumMax))## [1] TRUEall.equal(dfTest_v2 %>% pull(cumMax),
keyRatioDate %>% select(cumMax) %>% mutate(cumMax=ifelse(is.na(cumMax), 1, cumMax)) %>% pull(cumMax)
)## [1] TRUEFunctions are also written for plot creation:
plotDeltaFromMax <- function(df,
dfCtyData=NULL,
plotStateRatio=FALSE,
plotDeathStates=c(),
plotCaseStates=c()
) {
# FUNCTION ARGUMENTS:
# df: data frame or tibble containing ratios by entity and date
# dfCtyData: county-level per-capita data (not needed if only plotStateRatio is run)
# plotStateRatio: should the state ratio plot be created?
# plotDeathStates: vector of states to plot on the deaths metric (c() means do not plot)
# plotCaseStates: vector of states to plot on the cases metric (c() means do not plot)
# Plot 1: state ratios
if(isTRUE(plotStateRatio)) {
p1 <- df %>%
filter(!is.na(delMax)) %>%
group_by(state, name) %>%
summarize(minMax=min(delMax), .groups="drop") %>%
ggplot(aes(x=fct_reorder(state, -minMax, min), y=1+minMax)) +
geom_col(fill="lightblue") +
geom_text(aes(label=round(1+minMax, 2)), hjust=0) +
labs(title="Ratio of burden vs. cumulative maximum of burden (expected to be 1 for ascending sequence)",
subtitle="Lowest value of 1 + (value - cummax(value)) / max(value)",
y="Lowest value",
x=NULL
) +
coord_flip() +
facet_wrap(~name)
print(p1)
}
# Plots 2 and 3: Create case and death data
if((length(plotCaseStates) > 0) | (length(plotDeathStates) > 0)) {
dfPlot <- dfCtyData %>%
select(state, date, cases, deaths) %>%
filter(state %in% all_of(union(plotCaseStates, plotDeathStates))) %>%
group_by(state, date) %>%
summarize(across(c(cases, deaths), .fns=function(x) sum(x, na.rm=TRUE)), .groups="drop")
}
# Plot 2: Death states
if(length(plotDeathStates) > 0) {
p2 <- dfPlot %>%
filter(state %in% all_of(plotDeathStates)) %>%
ggplot(aes(x=date, y=deaths)) +
geom_line(aes(group=state)) +
facet_wrap(~state, scales="free_y") +
labs(title="Reported deaths by states with meaningfully non-ascending trend",
x=NULL,
y="Reported deaths"
)
print(p2)
}
# Plot 3: Case states
if(length(plotCaseStates) > 0) {
p3 <- dfPlot %>%
filter(state %in% all_of(plotCaseStates)) %>%
ggplot(aes(x=date, y=cases)) +
geom_line(aes(group=state)) +
facet_wrap(~state, scales="free_y") +
labs(title="Reported cases by states with meaningfully non-ascending trend",
x=NULL,
y="Reported cases"
)
print(p3)
}
}
plotDeltaFromMax(dfTest, plotStateRatio=TRUE)
plotDeltaFromMax(dfTest,
dfCtyData=cty_newdata_20220913$dfPerCapita,
plotDeathStates=c("IL", "TX", "MA", "NE"),
plotCaseStates=c("IL", "WY")
)
Missing vaccines data are also explored:
tmpVaxCounts <- readFromRDS("cty_vaxdata_20220709")$vaxFix %>%
count(date, name="vax0709") %>%
full_join(cty_vaxdata_20220914$vaxFix %>%
count(date, name="vax0914"),
by="date"
)
tmpVaxCounts %>%
pivot_longer(-c(date)) %>%
mutate(value=ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
labs(title="# Counties reporting vaccines by date", x=NULL, y="# Counties reporting") +
scale_color_discrete("Source Date")
There is only a small overlapping section of data. Older data will need to be merged for a complete dataset. Consistency of reported vaccines is also checked:
tempGetVax <- function(df, groupBy=c("date")) {
df %>%
group_by_at(all_of(groupBy)) %>%
summarize(vxc=sum(vxcpoppct*pop/100, na.rm=TRUE),
vxcgte18=sum(vxcgte18pct*popgte18/100, na.rm=TRUE),
vxcgte65=sum(vxcgte65pct*popgte65/100, na.rm=TRUE),
.groups="drop"
)
}
tmpVaxSum <- tempGetVax(cty_vaxdata_20220914$vaxFix) %>%
bind_rows(tempGetVax(readFromRDS("cty_vaxdata_20220709")$vaxFix), .id="src")
tmpVaxSum %>%
mutate(src=c("1"="14-SEP-22", "2"="09-JUL-22")[src]) %>%
pivot_longer(-c(src, date)) %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=src, color=src)) +
facet_wrap(~name) +
labs(title="Vaccinated by source, date, and age bucket", x=NULL, y="# Fully vaccinated") +
scale_color_discrete("Data From:")
Data volumes appear to be broadly consistent, further suggestive that pasting back missing historical data is reasonable.
Data Updates
The latest county-level burden data are downloaded:
readList <- list("usafCase"="./RInputFiles/Coronavirus/covid_confirmed_usafacts_downloaded_20221010.csv",
"usafDeath"="./RInputFiles/Coronavirus/covid_deaths_usafacts_downloaded_20221010.csv"
)
compareList <- list("usafCase"=readFromRDS("cty_newdata_20220913")$dfRaw$usafCase,
"usafDeath"=readFromRDS("cty_newdata_20220913")$dfRaw$usafDeath
)
# Use existing clusters
cty_newdata_20221010 <- readRunUSAFacts(maxDate="2022-10-08",
downloadTo=lapply(readList,
FUN=function(x) if(file.exists(x)) NA else x
),
readFrom=readList,
compareFile=compareList,
writeLog="./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log",
ovrwriteLog=TRUE,
useClusters=readFromRDS("cty_newdata_20210813")$useClusters,
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)## Rows: 3193 Columns: 992
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (989): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 30
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 2 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log##
##
## ***Differences of at least 0 and at least 0.1%
##
## 184 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log## Rows: 3193 Columns: 992
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): County Name, State, StateFIPS
## dbl (989): countyFIPS, 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
##
## *** File has been checked for uniqueness by: countyFIPS countyName state stateFIPS
##
##
## *** File has been checked for uniqueness by: countyFIPS stateFIPS date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 30
## Detailed differences available in: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log
##
## Checking for similarity of: county
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## 2 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log
##
##
## ***Differences of at least 0 and at least 0.1%
##
## 66 records
## Detailed output available in log: ./RInputFiles/Coronavirus/USAF_NewData_20221010_chk_v005.log
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType cases new_cases n
## <chr> <dbl> <dbl> <dbl>
## 1 before 3.73e+10 92832282 3154684
## 2 after 3.70e+10 90648951 3104296
## 3 pctchg 8.36e- 3 0.0235 0.0160
##
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 4
## isType deaths new_deaths n
## <chr> <dbl> <dbl> <dbl>
## 1 before 5.43e+8 1046659 3154684
## 2 after 5.24e+8 972745 3104296
## 3 pctchg 3.44e-2 0.0706 0.0160
## NULL
# Plot all counties based on closest cluster
sparseCountyClusterMap(cty_newdata_20221010$useClusters,
caption="Includes only counties with 25k+ population",
brewPalette="viridis"
)
# Save the refreshed file
saveToRDS(cty_newdata_20221010, ovrWriteError=FALSE)Vaccines data are also updated, though the process will need to integrate previous data:
cty_vaxdata_20221011 <- processCountyVaccines(loc="./RInputFiles/Coronavirus/county_vaccine_20221011.csv",
ctyList=cty_newdata_20221010,
minDateCD=c("2022-06-09", "2022-06-09"),
maxDateCD="2022-09-29"
)## Rows: 446971 Columns: 72
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): Date, FIPS, Recip_County, Recip_State, SVI_CTGY, Metro_status
## dbl (66): MMWR_week, Completeness_pct, Administered_Dose1_Recip, Administere...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.##
## Records from other than 50 states and DC:
## # A tibble: 9 × 2
## state n
## <chr> <int>
## 1 AS 136
## 2 FM 136
## 3 GU 272
## 4 MH 136
## 5 MP 136
## 6 PR 10756
## 7 PW 136
## 8 VI 545
## 9 <NA> 79
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
## Warning: Removed 16 rows containing non-finite values (stat_boxplot).
##
## Count of NA records by column
## state FIPS popgte65_minpop popgte65_maxpop popgte65_nnA
## 0 0 0 0 0
## n
## 0
##
## Records where minimum and maximum population differ# A tibble: 0 × 5
## # … with 5 variables: state <chr>, FIPS <chr>, age <chr>, minpop <dbl>,
## # maxpop <dbl>
## # ℹ Use `colnames()` to see all variable names
##
##
##
## Will run with parameters:
## burdenVar: cpm dpm
## vaxVar: vxcpoppct vxcpoppct
## minDateCD: 2022-06-09 2022-06-09
## maxDateCD: 2022-09-29 2022-09-29## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -292485037 -1737398 -20921 1895138 67166643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24796.753 2297.542 10.793 <2e-16 ***
## vaxMetric 9.448 35.514 0.266 0.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7738000 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 2.266e-05, Adjusted R-squared: -0.0002974
## F-statistic: 0.07078 on 1 and 3124 DF, p-value: 0.7902
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -291660235 -1751594 -20106 1897925 69792822
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 15606.10 8344.59 1.870 0.0615 .
## type>500k 31568.15 4919.11 6.417 1.60e-10 ***
## type100k-500k 21288.00 4903.26 4.342 1.46e-05 ***
## type25k-100k 21739.66 5529.94 3.931 8.63e-05 ***
## vaxMetric:type<25k 194.27 168.02 1.156 0.2477
## vaxMetric:type>500k -88.16 69.72 -1.264 0.2062
## vaxMetric:type100k-500k 67.89 78.76 0.862 0.3888
## vaxMetric:type25k-100k 70.74 104.37 0.678 0.4979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7741000 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.5302, Adjusted R-squared: 0.529
## F-statistic: 439.8 on 8 and 3118 DF, p-value: < 2.2e-16## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).
## Removed 16 rows containing missing values (geom_point).
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric, data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3549904 -14579 -2095 18933 693414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 352.2786 30.1110 11.70 <2e-16 ***
## vaxMetric -4.4866 0.4654 -9.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 101400 on 3124 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.02888, Adjusted R-squared: 0.02857
## F-statistic: 92.92 on 1 and 3124 DF, p-value: < 2.2e-16
##
##
## Call:
## lm(formula = get(burdenVar) ~ vaxMetric * type + 0 - vaxMetric,
## data = dfReg, weights = pop)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -3500417 -16774 -5431 14429 705700
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## type<25k 178.8397 108.8961 1.642 0.100629
## type>500k 413.3400 64.1940 6.439 1.39e-10 ***
## type100k-500k 147.3023 63.9871 2.302 0.021397 *
## type25k-100k 254.8892 72.1652 3.532 0.000418 ***
## vaxMetric:type<25k -0.4989 2.1926 -0.228 0.820011
## vaxMetric:type>500k -5.6202 0.9098 -6.177 7.36e-10 ***
## vaxMetric:type100k-500k -0.9261 1.0278 -0.901 0.367661
## vaxMetric:type25k-100k -2.1172 1.3620 -1.555 0.120155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 101000 on 3118 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.07933, Adjusted R-squared: 0.07696
## F-statistic: 33.58 on 8 and 3118 DF, p-value: < 2.2e-16# Save the refreshed file
saveToRDS(cty_vaxdata_20221011, ovrWriteError=FALSE)County-level data are post-processed:
cty_postdata_20221010 <- postProcessCountyData(lstCtyBurden=cty_newdata_20221010$dfPerCapita,
lstCtyVax=cty_vaxdata_20221011$vaxFix,
lstState=readFromRDS("cdc_daily_221002")$dfPerCapita
)##
## Parameter maxDate is: 2022-10-01
# Save the refreshed file
saveToRDS(cty_postdata_20221010, ovrWriteError=FALSE)The Alaska portion of usmap::plot_usmap() has an issue with Valdex county. The function is updated to allow for excluding problematic states:
# Updated to allow for include and exclude
makeBurdenDatePlot <- function(df,
keyVar,
timeLabel,
plotTitle=NULL,
varLabel=NULL,
varFloor=0,
varCeiling=+Inf,
varDivBy=1,
vecRename=c("countyFIPS"="fips"),
includeStates=c(),
excludeStates=c()
) {
# FUNCTION ARGUMENTS:
# df: a processed data frame with fips, asofDate, burden
# keyVar: character string for variable to be plotted
# timeLabel: character string for amount of time (e.g., "1-month" or "5-week")
# plotTitle: title for the plot (NULL means infer from other arguments)
# varLabel: label for the variable in plot scale (NULL means infer from other arguments)
# varFloor: minimum value to be allowed for variable (-Inf means no floor applied)
# varCeiling: maximum value to be allowed for variable (Inf means no ceiling applied)
# varDivBy: variable should be divivded by this for plotting
# vecRename: renaming vector to get desired variables in frame
# includeStates: specific states to be plotted, where c() means all states
# excludeStates: specific states NOT to be plotted (ignored if includeStates has length > 0)
# Create varLabel if passed as NULL
if(is.null(varLabel)) {
varLabel <- stringr::str_to_upper(stringr::str_extract(keyVar, "^[A-Za-z]*"))
if((varDivBy > 1) & isTRUE(all.equal(log10(varDivBy) %% 1, 0)))
varLabel <- paste0(varLabel, "(", stringr::str_replace(varDivBy, pattern="1", replacement=""), "s)")
else if (varDivBy != 1) varLabel <- paste0(varLabel, "(units of ", varDivBy, ")")
}
# Create plotTitle if passed as NULL
if(is.null(plotTitle))
plotTitle <- paste0(timeLabel,
" coronavirus ",
if(str_detect(stringr::str_to_upper(keyVar), pattern="CPM")) "cases" else "deaths",
" by county"
)
# Resolve includeStates and excludeStates
if(length(includeStates) > 0) {
if(length(excludeStates) > 0) {
cat("\nParamater excludeStates will be ignored since includeStates was passed\n")
excludeStates <- c()
}
invalidInclude <- setdiff(includeStates, c(state.abb, "DC"))
if(length(invalidInclude) > 0) {
cat("\nInvalid states passed in includeStates deleted:", paste(invalidInclude, collapse=", "), "\n")
includeStates <-setdiff(includeStates, invalidInclude)
}
}
# Create and return plot
p1 <- df %>%
colRenamer(vecRename=vecRename) %>%
mutate(burden=pmax(pmin(get(all_of(keyVar)), varCeiling), varFloor)/varDivBy) %>%
select(fips, burden, asofDate) %>%
usmap::plot_usmap(regions="counties", data=., values="burden", include=includeStates, exclude=excludeStates) +
labs(title=plotTitle,
subtitle=if(varFloor > -Inf | varCeiling < +Inf) "Floors and/or ceilings applied" else NULL,
caption="Source: USA Facts"
) +
scale_fill_continuous(paste0(varLabel, "\n", timeLabel), low="grey", high="red") +
facet_wrap(~asofDate) +
theme(legend.position="bottom")
p1
}
postProcessCountyData <- function(lstCtyBurden,
lstCtyVax,
lstState,
maxDate=NULL,
minDateBurden="2020-02-15",
minDateVax="2021-04-01",
includeStates=c(),
excludeStates=c()
) {
# FUNCTION ARGUMENTS:
# lstCtyBurden: list of processed county-level burden data (or a dfPerCapita file from this list)
# lstCtyVax: list of processed county-level vaccines data (or a vaxFix file from this list)
# lstState: list of processed state-level burden data (or a dfPerCapita file from this list)
# maxDate: maximum date to use for plotting (NULL means latest date in both lstCty and lstState)
# minDateBurden: earliest date for scoring burden similarity across files
# minDateVax: earliest date for scoring vaccine similarity across files
# includeStates: specific states to be plotted, where c() means all states
# excludeStates: specific states NOT to be plotted (ignored if includeStates has length > 0)
# Extract the relevant perCapita and vaxFix data if needed
if("list" %in% class(lstCtyBurden)) lstCtyBurden <- lstCtyBurden[["dfPerCapita"]]
if("list" %in% class(lstState)) lstState <- lstState[["dfPerCapita"]]
if("list" %in% class(lstCtyVax)) lstCtyVax <- lstCtyVax[["vaxFix"]]
# Get maxDate if not provided
if(is.null(maxDate)) maxDate <- min(max(lstCtyBurden$date, na.rm=TRUE), max(lstState$date, na.rm=TRUE))
cat("\nParameter maxDate is:", as.character(maxDate), "\n\n")
# Data for score similarity process
dfCompare <- compareStateSummedCounty(dfState=lstState,
dfCounty=lstCtyBurden,
inclStates=c(state.abb, "DC"),
dateThru=maxDate,
makePlot=FALSE,
returnData=TRUE
)
scoreSimilarity(dfCompare, minDate=minDateBurden, maxDate=maxDate, makeFacet=FALSE)
# Check differences in data sources
dfAllState <- integrateStateVaccine(lstCtyVax, statePerCap=lstState, treatNAZero=TRUE)
vaxDiff <- scoreVaxSimilarity(dfAllState, minDate=minDateVax, maxDate=maxDate, returnBaseData=TRUE)
# Create county-level burden data by quarters
dfRoll91 <- createBurdenCountyDate(lstCtyBurden,
maxDate=maxDate,
rollBy=months(c(0, -3, -6, -9)),
dateSpan=91
)
makeBurdenDatePlot(dfRoll91,
keyVar="cpm91",
timeLabel="3-month",
varCeiling=100000,
varDivBy=1000,
includeStates=includeStates,
excludeStates=excludeStates
) %>%
print()
makeBurdenDatePlot(dfRoll91,
keyVar="dpm91",
timeLabel="3-month",
varCeiling=1500,
includeStates=includeStates,
excludeStates=excludeStates
) %>%
print()
# Return the key elements
list(dfCompare=dfCompare, dfAllState=dfAllState, vaxDiff=vaxDiff, dfRoll91=dfRoll91)
}County-level data are post-processed:
cty_postdata_20221010_v2 <- postProcessCountyData(lstCtyBurden=cty_newdata_20221010$dfPerCapita,
lstCtyVax=cty_vaxdata_20221011$vaxFix,
lstState=readFromRDS("cdc_daily_221002")$dfPerCapita,
excludeStates="AK"
)##
## Parameter maxDate is: 2022-10-01
# Check equivalence of data files
identical(cty_postdata_20221010, cty_postdata_20221010_v2)## [1] TRUEAdditional post-processing steps are run:
# Step 1a: Burden comparisons for aggregated states
additionalCountyPostProcess(cty_postdata_20221010, p1CompareStates=c(state.abb, "DC"), p1AggData=TRUE)## Warning: Removed 6 row(s) containing missing values (geom_path).
# Step 1: Burden aggregation for key states
# Step 2: vaccine comparisons
# Step 3: Scoring updates (and errors)
# Step 4: New rolling data (28-day default with ceilings 50000 CPM, 500 DPM)
additionalCountyPostProcess(cty_postdata_20221010,
p1CompareStates=c("GA", "FL", "NE"),
p2VaxStates=c("MA", "HI", "TX", "VA", "VT", "GA", "CO", "SD"),
p3VaxTimes=sort(c("2022-01-01", "2022-09-28")),
p4DF=cty_newdata_20221010$dfPerCapita
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 1 row(s) containing missing values (geom_path).
The function is updated to allow for excluding states (such as AK in this case where Valdez County issues in usmap cause an NA issue):
additionalCountyPostProcess <- function(lstPost,
p1CompareStates=c(),
p1AggData=FALSE,
p2VaxStates=c(),
p3VaxTimes=c(),
p4DF=NULL,
p4MaxDate=NULL,
p4RollBy=months(c(0, -1, -2, -3)),
p4DateSpan=28,
p4CPMCeiling=50000,
p4DPMCeiling=500,
includeStates=c(),
excludeStates=c()
) {
# FUNCTION ARGUMENTS:
# lstPost: list of post-processed county data
# p1CompareStates: states that should be compared vs. summed county
# p1AggData: boolean, should the comparison states all be aggregated to a single comparison?
# p2VaxStates: states that should be compared for vaccine evolution
# p3VaxTimes: character vector of form c(minDate, maxDate) for time period to score vaccine similarity
# p4DF: data frame for creating rolling data (NULL means do not run)
# p4MaxDate: maximum date for rolling analysis (NULL means use maximum date in p4DF minus 1 day)
# p4RollBy: time periods to roll back for analysis
# p4DateSpan: size of windows for rolling analysis
# p4CPMCeiling: ceiling for plots on CPM (all values at or above this will be the same color)
# p4DPMCeiling: ceiling for plots on DPM (all values at or above this will be the same color)
# includeStates: specific states to be plotted, where c() means all states
# excludeStates: specific states NOT to be plotted (ignored if includeStates has length > 0)
# 1. Plotting state vs. summed county for key states
if(length(p1CompareStates) > 0) {
compareStateSummedCounty(lstAll=lstPost[["dfCompare"]],
inclStates=p1CompareStates,
createData=FALSE,
aggData=p1AggData
)
}
# 2. Plot differences in vaccines data if needed
if(length(p2VaxStates) > 0)
stateAgeVaxEvolution(lstPost[["dfAllState"]], keyState=p2VaxStates)
# 3. Check vaccine similarity scoring on a different time period
if(length(p3VaxTimes) > 0) {
if(length(p3VaxTimes) != 2 | p3VaxTimes[2] < p3VaxTimes[1])
cat("\np3VaxTimes should be c(minDate, maxDate), skipping this step due to bad parameter\n")
else
scoreVaxSimilarity(lstPost[["dfAllState"]], minDate=p3VaxTimes[1], maxDate=p3VaxTimes[2])
}
# 4. Additional rolling data as needed
if(!is.null(p4DF)) {
if(is.null(p4MaxDate)) p4MaxDate <- max(p4DF$date) - lubridate::days(1)
dfRoll <- createBurdenCountyDate(p4DF, maxDate=p4MaxDate, rollBy=p4RollBy, dateSpan=p4DateSpan)
makeBurdenDatePlot(dfRoll,
keyVar=paste0("cpm", p4DateSpan),
timeLabel=paste0(p4DateSpan, "-day"),
varCeiling=p4CPMCeiling,
varDivBy=1000,
includeStates=includeStates,
excludeStates=excludeStates
) %>%
print()
makeBurdenDatePlot(dfRoll,
keyVar=paste0("dpm", p4DateSpan),
timeLabel=paste0(p4DateSpan, "-day"),
varCeiling=p4DPMCeiling,
includeStates=includeStates,
excludeStates=excludeStates
) %>%
print()
}
}Additional post-processing steps are re-run:
# Step 1a: Burden comparisons for aggregated states
additionalCountyPostProcess(cty_postdata_20221010, p1CompareStates=c(state.abb, "DC"), p1AggData=TRUE)## Warning: Removed 6 row(s) containing missing values (geom_path).
# Step 1: Burden aggregation for key states
# Step 2: vaccine comparisons
# Step 3: Scoring updates (and errors)
# Step 4: New rolling data (28-day default with ceilings 50000 CPM, 500 DPM)
additionalCountyPostProcess(cty_postdata_20221010,
p1CompareStates=c("GA", "FL", "NE"),
p2VaxStates=c("MA", "HI", "TX", "VA", "VT", "GA", "CO", "SD"),
p3VaxTimes=sort(c("2022-01-01", "2022-09-28")),
p4DF=cty_newdata_20221010$dfPerCapita,
excludeStates=c("AK")
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 1 row(s) containing missing values (geom_path).
Restatement issues are explore:
# Check for states
dfTest_20221010_state <- findDeltaFromMax(cty_newdata_20221010$dfPerCapita,
groupBy="state",
numVar=c("cases", "deaths")
)
# Check for counties
dfTest_20221010_cty <- findDeltaFromMax(cty_newdata_20221010$dfPerCapita,
groupBy=c("countyFIPS", "state"),
numVar=c("cases", "deaths")
)
# Explore state differences
plotDeltaFromMax(dfTest_20221010_state, plotStateRatio=TRUE)
plotDeltaFromMax(dfTest_20221010_state,
dfCtyData=cty_newdata_20221010$dfPerCapita,
plotDeathStates=c("IL", "MA", "NE"),
plotCaseStates=c("IL", "WY")
)
Counties are also explored for restatement, using a combination of value and ratio:
dfRatio_cty <- dfTest_20221010_cty %>%
group_by(countyFIPS, state, name) %>%
filter(delMax==min(delMax)) %>%
filter(date==max(date)) %>%
ungroup()
dfRatio_cty## # A tibble: 6,284 × 8
## countyFIPS state date name value ratMax cumMax delMax
## <chr> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 01001 AL 2022-04-19 cases 15755 0.855 0.999 -0.000869
## 2 01001 AL 2022-03-27 deaths 209 0.917 0.995 -0.00439
## 3 01003 AL 2022-04-19 cases 55564 0.845 1.00 -0.000228
## 4 01003 AL 2021-04-14 deaths 300 0.420 0.997 -0.00140
## 5 01005 AL 2021-06-27 cases 2344 0.339 0.999 -0.000289
## 6 01005 AL 2021-05-19 deaths 56 0.544 0.982 -0.00971
## 7 01007 AL 2021-04-14 cases 2559 0.340 0.998 -0.000663
## 8 01007 AL 2021-04-12 deaths 58 0.537 0.967 -0.0185
## 9 01009 AL 2021-11-11 cases 10494 0.616 0.995 -0.00317
## 10 01009 AL 2021-04-22 deaths 133 0.516 0.985 -0.00775
## # … with 6,274 more rows
## # ℹ Use `print(n = ...)` to see more rows# Explore ratios of deaths by county - IL/MA/NE vs. others
dfRatio_cty %>%
filter(name=="deaths", value > 0) %>%
mutate(type=case_when(state=="IL" ~ "IL", state %in% c("MA", "NE") ~ "MA/NE", state=="WY" ~ "WY", TRUE ~ "Other"),
rat=1+delMax
) %>%
ggplot(aes(x=rat)) +
geom_histogram(aes(fill=type)) +
facet_wrap(~type, nrow=2, scales="free_y") +
labs(x="Lowest ratio of (deaths-cummax(deaths))/max(deaths) by county",
y="# Counties",
title="Death ratios by county (1.0 means strictly non-descending, 0.0 means reports 0 after max)"
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Explore ratios of cases by county - IL/WY vs. others
dfRatio_cty %>%
filter(name=="cases", value > 0) %>%
mutate(type=case_when(state=="IL" ~ "IL", state %in% c("MA", "NE") ~ "MA/NE", state=="WY" ~ "WY", TRUE ~ "Other"),
rat=1+delMax
) %>%
ggplot(aes(x=rat)) +
geom_histogram(aes(fill=type)) +
facet_wrap(~type, nrow=2, scales="free_y") +
labs(x="Lowest ratio of (cases-cummax(cases))/max(cases) by county",
y="# Counties",
title="Case ratios by county (1.0 means strictly non-descending, 0.0 means reports 0 after max)"
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Case data generally has more restatement than deaths data. IL, MA, and NE all have significant county-level changes for deaths, while WY has the most counties reporting large changes in cases
A sample of counties with high death restatements are plotted:
dfRatio_cty %>%
filter(name=="deaths", delMax < -0.01, delMax*value < -100) %>%
select(countyFIPS) %>%
left_join(cty_newdata_20221010$dfPerCapita, by="countyFIPS") %>%
ggplot(aes(x=date, y=deaths)) +
geom_line() +
facet_wrap(~paste0(countyFIPS, " (", state, ")"), scales="free_y")
The issue in IL is driven almost exclusively by 17031, which restated away around 50% of deaths. The issue in MA appears to be consistent across counties, with the restated data somewhat close to on trend prior to the spike. Two counties in TX and NJ have an erroneous spike, while one county in TX (48141) restated away almost all deaths
A sample of counties with high case restatements are plotted:
dfRatio_cty %>%
filter(name=="cases", delMax < -0.01, delMax*value < -2000) %>%
select(countyFIPS) %>%
left_join(cty_newdata_20221010$dfPerCapita, by="countyFIPS") %>%
ggplot(aes(x=date, y=cases)) +
geom_line() +
facet_wrap(~paste0(countyFIPS, " (", state, ")"), scales="free_y")
Many counties with high case restatements appear to mainly follow trend at a glance. Significant off-trend deviations occur in 17031 (IL), 25007 (MA), 48139 (TX), 48141 (TX), 48189 (TX), and 48279 (TX). The deviations in Wyoming appear to be one-time issues.
Data with and without anomalous counties can then be plotted:
anom_cty <- dfRatio_cty %>%
filter(name=="cases") %>%
mutate(bigPct=(delMax < -0.05), bigVol=(delMax*value < -2000))
anom_cty %>%
count(bigPct, bigVol)## # A tibble: 4 × 3
## bigPct bigVol n
## <lgl> <lgl> <int>
## 1 FALSE FALSE 3011
## 2 FALSE TRUE 6
## 3 TRUE FALSE 113
## 4 TRUE TRUE 12anom_cty %>%
select(countyFIPS, state, bigPct, bigVol) %>%
inner_join(cty_newdata_20221010$dfPerCapita, by=c("state", "countyFIPS")) %>%
mutate(type=case_when(bigVol ~ "2) Big volume change",
bigPct ~ "3) Big percent change",
TRUE ~ "1) Low/no percent change"
)
) %>%
group_by(type, date) %>%
summarize(across(c(cases, deaths), sum), .groups="drop") %>%
ggplot(aes(x=date, y=cases)) +
geom_line() +
facet_wrap(~type, scales="free_y") +
labs(title="Sum of cases by county type",
x=NULL,
y="Sum of cases",
subtitle="Big volume change is at least 2000 cases, big percentage change is at least 5% (but not 2000 cases)"
)
Case data appear reasonable after anomalies are removed. The large percentage changes make some impact but appear to generally average out and remain on trend. The large volume changes appear to be anomalies that impact trends, especially in the most recent time periods.
The process is converted to functional form:
plotByRestatement <- function(dfRatio,
dfBurden,
idVars=c("countyFIPS", "state"),
keyMetric="cases",
keyMetricBurden=keyMetric,
delMaxHurdle=0,
bigVolHurdle=0,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# dfRatio: file containing key metrics for determining restatement status
# dfBurden: the burden data by geography
# idVars: variables that identify a geographical unit
# keyMetric: name of variable in the ratio file
# keyMetricBurden: name of variable in the burden file
# delMaxHurdle: cutoff for determining large delMax
# bigVolHurdle: cutoff for determining large delMax*value
# returnData: boolean, should dfSeg be returned?
# Create segment data
dfSeg <- dfRatio %>%
filter(name==keyMetric) %>%
mutate(bigPct=(delMax < delMaxHurdle), bigVol=(delMax*value < bigVolHurdle))
# Report on segment data
dfSeg %>%
count(bigPct, bigVol) %>%
print()
# Create and print plot
p1 <- dfSeg %>%
select(c(all_of(idVars), "bigPct", "bigVol")) %>%
inner_join(dfBurden, by=all_of(idVars)) %>%
mutate(type=case_when(bigVol ~ "2) Big volume change",
bigPct ~ "3) Big percent change",
TRUE ~ "1) Low/no percent change"
)
) %>%
group_by(type, date) %>%
summarize(across(all_of(keyMetricBurden), sum), .groups="drop") %>%
ggplot(aes_string(x="date", y=keyMetricBurden[1])) +
geom_line() +
facet_wrap(~type, scales="free_y") +
labs(title="Sum of cases by segment",
x=NULL,
y="Sum of cases",
subtitle=paste0("Big volume change is more than ",
-bigVolHurdle,
" ",
keyMetricBurden[1],
", big percentage change is at least ",
round(-100*delMaxHurdle),
"% (but not ",
-bigVolHurdle,
" ",
keyMetricBurden[1],
")"
)
)
print(p1)
# Return data if requested
if(isTRUE(returnData)) return(dfSeg)
}
dfSegTest <- plotByRestatement(dfRatio_cty,
dfBurden=cty_newdata_20221010$dfPerCapita,
keyMetric="cases",
delMaxHurdle=-0.05,
bigVolHurdle=-2000,
returnData=TRUE
)## # A tibble: 4 × 3
## bigPct bigVol n
## <lgl> <lgl> <int>
## 1 FALSE FALSE 3011
## 2 FALSE TRUE 6
## 3 TRUE FALSE 113
## 4 TRUE TRUE 12
identical(anom_cty, dfSegTest)## [1] TRUEA function is also written to create the main restatement dataset:
createRestatementData <- function(df,
geoType="county",
idVars=NULL,
numVars=c("cases", "deaths"),
varPeak="delMax",
fnPeak=min,
makeIntermediate=TRUE,
returnIntermediate=FALSE
) {
# FUNCTION ARGUMENTS:
# df: data frame containing relevant per-capita data OR previously made dfIntermediate
# geoType: the type of data (only "county" and "state" are supported)
# idVars: the ID variables for a geographical unit in df (NULL means infer from geoType)
# numVars: numeric variables for calculating restatement amounts
# varPeak: variable to be used for calculating whether restatement occurred
# fnPeak: function to be applied to determine where 'most' restatement occurred
# makeIntermediate: boolean, should dfIntermediate be created? Otherwise, df is treated as dfIntermediate
# returnIntermediate: boolean, should the process be stopped and just dfIntermediate returned?
# Infer idVars if passed as NULL
if(is.null(idVars)) {
if(geoType=="county") idVars <- c("countyFIPS", "state")
else if(geoType=="state") idVars <- c("state")
else error("\nFunction can only infer for geoType of 'county' or 'state'\n")
}
# Create intermediate data frame using findDeltaFromMax() OR as passed
if(isTRUE(makeIntermediate)) df <- findDeltaFromMax(df, groupBy=idVars, numVar=numVars)
# If returnIntermediate is TRUE, return that frame and stop the function
if(isTRUE(returnIntermediate)) return(df)
# Create and return the ratio data otherwise
df %>%
group_by_at(all_of(c(idVars, "name"))) %>%
filter(get(varPeak)==fnPeak(get(varPeak))) %>%
filter(date==max(date)) %>%
ungroup()
}
# Check data creation process for counties
dfInter_cty <- createRestatementData(cty_newdata_20221010$dfPerCapita,
geoType="county",
makeIntermediate=TRUE,
returnIntermediate=TRUE
)
identical(dfInter_cty, dfTest_20221010_cty)## [1] TRUE# Check data creation process for states
dfInter_state <- createRestatementData(cty_newdata_20221010$dfPerCapita,
geoType="state",
makeIntermediate=TRUE,
returnIntermediate=TRUE
)
identical(dfInter_state, dfTest_20221010_state)## [1] TRUEdfRatio_cty_chk <- createRestatementData(dfInter_cty,
geoType="county",
makeIntermediate=FALSE,
returnIntermediate=FALSE
)
identical(dfRatio_cty_chk, dfRatio_cty)## [1] TRUEdfRatio_state <- createRestatementData(dfInter_state,
geoType="state",
makeIntermediate=FALSE,
returnIntermediate=FALSE
)
dfRatio_state## # A tibble: 102 × 7
## state date name value ratMax cumMax delMax
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AK 2021-06-14 cases 68383 0.251 0.982 -0.00448
## 2 AK 2021-08-21 deaths 382 0.292 0.972 -0.00842
## 3 AL 2022-04-19 cases 1298473 0.851 1.00 -0.000239
## 4 AL 2021-04-09 deaths 10686 0.522 1.00 -0.000244
## 5 AR 2022-10-06 cases 921985 1 1 0
## 6 AR 2021-04-01 deaths 5636 0.460 0.994 -0.00269
## 7 AZ 2022-10-06 cases 2275235 1 1 0
## 8 AZ 2022-03-08 deaths 27708 0.928 0.991 -0.00797
## 9 CA 2020-02-02 cases 27 0.00000259 0.0327 -0.0000767
## 10 CA 2021-12-27 deaths 75398 0.794 1.00 -0.000347
## # … with 92 more rows
## # ℹ Use `print(n = ...)` to see more rowsRatio data can then be plotted:
dfRatio_state %>%
ggplot(aes(x=fct_reorder(state, delMax), y=delMax)) +
geom_col() +
geom_text(aes(label=paste0(round(100*delMax, 0), "%")), hjust=1, size=3) +
coord_flip() +
facet_wrap(~name) +
labs(title="Maximum downward restatement from previous peak vs. maximum value for state",
x=NULL,
y=NULL,
subtitle="Data through September 2022"
)
As observed previously, most states have at most modest restatement in the USAF data